I am a systems, and computational neuroscientist at MIT.
Primary research work is on the neurophysiology and computational modeling on visual attention, eye movements and executive functions, and the theory of neural networks.
Interests span math, science, history, politics, economics, and philosophy.
Our paper, "Stimulus representations in visual cortex shaped by spatial attention and microsaccades" was just published in the Proceedings of the National Academy of Sciences (PNAS).
https://www.pnas.org/doi/10.1073/pnas.2420704122
#Attention #VisualAttention #SpatialAttention #VisualCortex #Microsaccades #Saccades #EyeMovements #Representation #Dynamics #ActiveCognition #ActiveSensing #Decoding #V4cortex #ITcortex #Pulvinar #Neuroscience #PNAS
We are organizing and hosting the USA Memory Championship Organization's "Tournament of Memory Champions" at the Kresge Auditorium at MIT on November 10th(Sunday).
During the event, we will have short scientific talks about the benefits of mnemonic and cognitive training in health and in education.
The event is free and open to the public!
Please register here, and join us if you are in the Boston area:
https://www.eventbrite.com/e/the-mit-tournament-of-memory-champions-tickets-1042035515277
#HumanMemory #BiologicalMemory #Neuroscience #Biology #Mnemonics #CognitiveTraining #MIT #USAMC
Interested in the mysteries of human memory? Come check out the MIT Tournament of Memory Champions on November 10th at Kresge Auditorium. Organized by a few neuro-friends.
Wrote an essay on memory after a similar even in 2017. 👇
Just heard the news that the great great James C Scott is no more! A singular figure, who created fields of inquiry all his own.
Reading "Seeing like a state" was one of those worldview-changing works. His idea of "metis" (i.e., experiential and practical knowledge) as opposed to epistemic knowledge (science etc.,) along with "legibility" are still relevant. In a world salivating about AGI etc., etc., metis (as this adaptive, and ever changing knowledge system) tells us something very profound about human societies, and knowledge (ultimately illegible to machinations, thus helping freedom to thrive).
If you haven't read his works, I encourage everyone to. And if you want to want to start somewhere, and know how he writes (and eviscerates), here is a sample:
https://www.lrb.co.uk/the-paper/v35/n22/james-c.-scott/crops-towns-government
#PoliticalPhilosophy #State #Anarchism #Metis #ComparitivePolitics #Anthropology #Modernism #Knowledge #Politics #Power
Good riddance to what was a colossal waste of money, energy, resources, and any sane person's time, intellect, and attention. To even call these as exploratory projects is a disservice to human endeavor.
"Future of humanity", it seems. These guys can't even predict their next bowel movement, but somehow prognosticate about the long term future of humanity, singularity blah blah. This is what "philosophy" has come to with silicon valley and its money power: demented behavior is incentivized, douchery is rationalized, while reason is jettisoned.
#Longtermism #EffectiveAltruism #Futurism #Philosophy #ExistentialRisk #ArtificialIntelligence
Great write-up by @annaleen on the modern history of the pseudoscience of "brainwashing" and how it has been (/tried to be) used for mostly nefarious ends.
We can say this "psychopolitics" is part and parcel of what the great political scientist Richard Hofstadter termed the "paranoid style in American politics".
Awesome to see a mention of Liang Qichao and how his term "xinao" (wash-brain) which meant modernization was usurped and became a negative connotation. He was one of the great early reformers who wanted to modernize Chinese philosophy by seeking a radical break from Confucianism. Pankaj Mishra's "From the ruins of empire" does a great job of his intellectual response to western imperialism in remaking Asia.
First time also hearing/reading about "stochastic terrorism".
(H/T: @DrYohanJohn )
#Brainwashing #Pseudoscience #Neuroscience #History #MindControl #LiangQichao #AmericanPolitics #ConspiracyTheories
With elections in India in full swing, this is fantastic infographic on India and its elections from the very beginning (1952 to present) in The Hindu. This is like a crash course of Indian history since independence!
Whatever your political ideological stance, India remains the most important, interesting, and unlikely experiment in politics.
I was interviewed by The Economist's Babbage podcast on their series, "The science that built AI" last month. My hour long conversation was edited to about six minutes!
I am glad they edited/fit my conversation as taking the perspective that this big data, big compute driven deep-net approach is orthogonal to human/biological vision. And that, without incorporating biological principles (in this case, vision), autonomous visual navigation systems (i.e., self-driving cars) are unlikely and/or limited.
Unfortunately, the podcast requires a subscription to The Economist (I too had to access it from my university account!). But if you do have access, let me know what you think!
https://open.spotify.com/episode/4adN2gVRkQctA55Q0xswiO
#Neuroscience #History #AI #Deepnets #BiologicalIntelligence #BiologicalVision #HumanVision #MachineVision #TheEconomist #Babbage #MachineLearning
Listen to this episode from Babbage from The Economist on Spotify. What made AI take off? A decade ago many computer scientists were focused on building algorithms that would allow machines to see and recognise objects. In doing so they hit upon two innovations—big datasets and specialised computer chips—that quickly transformed the potential of artificial intelligence. How did the growth of the world wide web and the design of 3D arcade games create a turning point for AI?This is the third episode in a four-part series on the evolution of modern generative AI. What were the scientific and technological developments that took the very first, clunky artificial neurons and ended up with the astonishingly powerful large language models that power apps such as ChatGPT?Host: Alok Jha, The Economist’s science and technology editor. Contributors: Fei-Fei Li of Stanford University; Robert Ajemian and Karthik Srinivasan of MIT; Kelly Clancy, author of “Playing with Reality”; Pietro Perona of the California Institute of Technology; Tom Standage, The Economist’s deputy editor.On Thursday April 4th, we’re hosting a live event where we’ll answer as many of your questions on AI as possible, following this Babbage series. If you’re a subscriber, you can submit your question and find out more at economist.com/aievent. Listen to what matters most, from global politics and business to science and technology—subscribe to Economist Podcasts+For more information about how to access Economist Podcasts+, please visit our FAQs page or watch our video explaining how to link your account. Hosted on Acast. See acast.com/privacy for more information.
Hi all, we would like to introduce you to Spyglass (https://github.com/LorenFrankLab/spyglass) - our software framework for creating reproducible data analysis and data sharing for neuroscience research (spearheaded by Kyu Hyun Lee and myself, but really a group effort by the Frank lab).
Try it for yourself without any setup at https://spyglass.hhmi.2i2c.cloud/ thanks to support from @2i2c_org, @RapidScience, @HHMINEWS. Note that there might be a slight wait for things to load.
We all know how hard it is to keep track of all the parameters and code that go into processing neuroscience data. These choices fundamentally affect the outcomes of a paper, but we have few reliable ways of recording what those choices are.
One reason for this is neuroscience data is complex and writing good code that keeps track of these choices is hard. Researchers typically create ad-hoc pipelines to existing tools for themselves, but this is time consuming and potentially error prone.
We built Spyglass to make it easy for researchers to process and track their data. We make it possible for users to spikesort and curate their data using different spike sorters via @spikeinterface, track the pose of animals via @DeepLabCut, or more complex analyses like decoding
We make all this possible using the @NeurodataWB format. We believe that starting with data in NWB and keeping analyses within this format unlocks huge potential to take advantage of tools that rely on this standard. This makes it easy to share your data on @DANDIarchive.
We realized that simply processing data is not enough. You have to visualize your data to know processing worked, but there can be a lot of data with many data types. We make this easy using figurl - an interactive web-based visualization tool by Jeremy Magland (@FlatironInst).
For example, you can visualize spike sorting curation: https://figurl.org/f?v=gs://figurl/spikesortingview-10&d=sha1://1fa0b4a1663323b49b6f1934d79ca9f67779bda8&s=%7B%22initialSortingCuration%22:%22sha1://51b950cad7d97f26aaf807ba234e4b41ffade4ef%22,%22sortingCuration%22:%22gh://LorenFrankLab/sorting-curations/main/mcoulter/molly20220316_.nwb_r1_r2/15/curation.json%22%7D&label=molly20220316_.nwb_r1_r2_15_franklab_tetrode_hippocampus%20molly20220316_.nwb_r1_r2_15_franklab_tetrode_hippocampus_13f7a6a2_spikesorting
or visualize ripple detection: https://figurl.org/f?v=gs://figurl/spikesortingview-10&d=sha1://f94ea807087b446aa0ff7f1993fbafe7a9066f79&label=Ripple%20Detection&zone=franklab.default
or even visualize decoding of hippocampal mental representations: https://figurl.org/f?v=gs://figurl/spikesortingview-10&d=sha1://3990d47cfcfbe426fae203659479e55d7b08980f&label=j1620210710_clusterless_decode&zone=franklab.default
Finally, neuroscience research is becoming more collaborative within and across labs, but sharing data is still difficult. Spyglass makes it easy for you to share data by allowing collaborators to access the database and seamlessly download data via the cloud.
If you want to find out more, please read our preprint: https://www.biorxiv.org/content/10.1101/2024.01.25.577295v2
or view our documentation and tutorials: https://lorenfranklab.github.io/spyglass/latest/
Yohan John 🤖🧠
Eric Denovellis